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🦉DVC

@DVCorg

Sep 2, 2022 • 8 tweets • 4 min read • Read on X

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@iterativeai

For all the devs out there willing to contribute to DVC, here is a quick guide to contributing to iterative/dvc repo
🐞 Open a new issue
💻 Set up a dev environment
🍴 Fork iterative/dvc
🧪 Add tests and run them locally
⬆️ Submit a pull request
@iterativeai

🧵 [1/7]

🐞 Open a new issue

Open a new issue in the issue tracker, whether it be a bug report or a feature request. 👇🏽
github.com/iterative/dvc/…

🧵[2/7]

🍴 Fork iterative/dvc

Fork iterative/dvc and then clone it into your local computer to start contributing.

🧵[3/7]

💻 Set up a dev environment

Make sure that you have Python 3.8 or higher installed. Install DVC in editable mode with ‘pip install -e ".[all,tests]" '.
All this is preferably in a virtual environment.

🧵 [4/7]

🧪 Add tests and run them locally

We have unit tests in "tests/unit/" and functional tests in "tests/func/". Consider writing the former to ensure complicated functions and classes behave as expected.
The simplest way to run tests is using the command "python-m tests".

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Well done 😊. We’re just about there…

⬆️ Submit a pull request
And finally, submit a pull request, referencing any issues it addresses and get it reviewed and merged. 🎉

🧵[6/7]

❤️Thanks for reading

We all could make #DVC more helpful for everyone together 🤝
Go ahead, fork DVC and try resolving an issue 👇🏽
github.com/iterative/dvc

🧵 [7/7]

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🦉DVC

@DVCorg

May 18, 2023

@FuzzyLabsAI

𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆 𝗦𝗽𝗼𝘁𝗹𝗶𝗴𝗵𝘁!!

We were privileged to have Matt Squire from @FuzzyLabsAI, a renowned expert in the MLOps space, as our guest.

During the session, Matt shared his insights on the benefits of open-source MLOps tools and how they can help businesses.

🧵🧵

In his 𝗣𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻, Matt provided a comprehensive breakdown of the MLOps tool space, categorizing it into SaaS platforms, fully open source, and partly open source tools.

He went on to describe the defining traits of open source and why he believes it to be the best choice for MLOps, including its flexibility, cost-effectiveness, and agility.

Matt's views on open-source MLOps tools generated a lot of engagement from our community members.

Read 4 tweets

🦉DVC

@DVCorg

May 17, 2023

@huggingface

Several data practitioners have asked for a tutorial video from us on DVC, and we are glad to make this available.

In this video, you'll discover how to use the DVCLive and @huggingface datasets to create a Tweet Sentiment Analyzer.

🧵🧵🧵

Do you want to learn more about using real-time metrics in machine learning experiments? Look nowhere else!

In this 𝘁𝘂𝘁𝗼𝗿𝗶𝗮𝗹, we'll show you how to use DVCLive and the Hugging Face Dataset to build a potent Twitter sentiment analyzer.

𝐃𝐕𝐂𝐋𝐢𝐯𝐞, a library from 𝐃𝐕𝐂, gives you the ability to easily monitor your ML experiments. You will be able to easily grasp the performance of your model at every stage thanks to its real-time metrics.

Read 9 tweets

🦉DVC

@DVCorg

Apr 25, 2023

We are happy to announce something exciting to the ML/AI Community🚨

DVC has just released its new integration with Optuna, enabling you to streamline and optimize your hyperparameter search process while keeping track of every step with version control.

Our users have been requesting this integration for a while, and we're thrilled to deliver!

With the DVC's extension for VS Code, you can easily monitor and analyze your results, saving you time and effort in your machine learning workflow.

Try out our new integration today and see the benefits for yourself! 🚀

This is the link to the video -

In the video, we explain how to use Optuna with Keras and view each iteration as an experiment in DVC's experiment table and plots. 🔍📈

Read 4 tweets

🦉DVC

@DVCorg

Apr 20, 2023

@DVCorg

Here's a comprehensive thread on how to build a seamless batch-scoring experience.

You will learn how to:

🤖 Design ML pipelines with @DVCorg

🐈 Training with @gitlab and CML

🧪 ML experiments with @Iterativeai Studio

🚢 Deployments with @ApacheAirflow

🧵 [1/6]

🤖 Design #ML pipelines using #DVC! Streamline your process with the following steps:

1️⃣ Data prep

2️⃣ Feature engineering

3️⃣ Model training

4️⃣ Model evaluation

DVC optimizes run time & tracks changes for a more efficient workflow!

Get started with 'dvc exp run' 🚀

🧵 [2/6]

Read 14 tweets

🦉DVC

@DVCorg

Feb 28, 2023

What is your favourite IDE/Code Editor for Machine Learning?

1. Jupyter Lab/Notebook
2. R Studio
3. Pycharm
4. VS Code
5. Spyder
6. Others(name it)

@DVCorg

Follow @DVCorg

A lot of people keep mentioning @code

Check out this @code Extension for Machine learning experiment management -

marketplace.visualstudio.com/items?itemName…

@code

These are the Top @code extensions for Machine learning in 2023.

1. GitHub Copilot
2. DVC
3. GitLens
4. Jupyter
5. Jupyter Notebook Renderers
6. Python
7. Pylance
8. Python Indent
9. Indent-rainbow
10. Todo MD

You can also share other ones to educate others.

Read 5 tweets

🦉DVC

@DVCorg

Feb 13, 2023

Woah! Been here? Is deep learning model training going horribly wrong? 🙋🏽‍♂️

Iterative Studio makes this easy to see so you don't waste time and resources!

🧵 1/7

With Iterative Studio and DVCLive, you can monitor the progress of your long-running experiments against others that you or your team have performed. All are easily accessed at work, at home, or by the rest of your team on the project.

🧵2/7

You provide a couple environment variables for your model training job:

You can enter your STUDIO_TOKEN and dvc exp run if running locally

🧵3/7

Read 7 tweets

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